Panthera pardus fusca) across Indian subcontinent · Among all the subspecies, the Indian leopard (P. p. fusca) retains the largest population size and range outside Africa (Jacobson

Title: Genetic analyses reveal population structure and recent decline in leopards

(Panthera pardus fusca) across Indian subcontinent

Supriya Bhatt1#, Suvankar Biswas1#, Krithi K. Karanth2, 3, 4, Bivash Pandav1, Samrat

Mondol1*#

1 Wildlife Institute of India, P.O. Box 18, Chandrabani, Dehradun, India

2 Centre for Wildlife Studies, 224 Garden Apartments, Bangalore, India

3 Wildlife Conservation Society, Bronx, New York, United States of America

4 Nicholas School of Environment, Duke University, Durham, United States of America

#Equal contributor

*Corresponding author: Samrat Mondol, Wildlife Institute of India, P.O. Box 18,

Chandrabani, Dehradun, India. Email- [email protected]

Running title: Population genetics and demography of Indian leopards

.CC-BY-NC-ND 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted August 24, 2019. . https://doi.org/10.1101/746081doi: bioRxiv preprint

https://doi.org/10.1101/746081

http://creativecommons.org/licenses/by-nc-nd/4.0/

Abstract

Large carnivores maintain the stability and functioning of ecosystems. Currently, many

carnivore species face declining population sizes due to natural and anthropogenic pressures.

The leopard, Panthera pardus, is probably the most widely distributed and adaptable large

carnivore, still persisting in most of its’ historic range. However, we lack subspecies level data

on country or regional scale on population trends, as ecological monitoring approaches are

difficult to apply on such wide-ranging species. We used genetic data from leopards sampled

across the Indian subcontinent to investigate population structure and patterns of demographic

decline. Our genetic analyses revealed four distinct subpopulations corresponding to Western

Ghats, Deccan Plateau-Semi Arid, Shivalik and Terai region of north Indian landscapes, each

with high genetic variation. Coalescent simulations with 13 microsatellite loci revealed a 75-

90% population decline in between 120-200 years ago across India, possibly human induced.

Population-specific estimates of genetic decline are in concordance with ecological estimates of

local extinction probabilities in four sub-populations obtained from occupancy modelling of

historic and current distribution of leopards in India. Our results confirm population decline of a

widely distributed, adaptable large carnivore. We re-iterate the relevance of indirect genetic

methods for such species, and recommend that detailed, landscape-level ecological studies on

leopard populations are critical to future conservation efforts. Our approaches and inference are

relevant to other widely distributed, seemingly unaffected carnivores such as the leopard.


https://doi.org/10.1101/746081


Introduction

Large carnivores are critical to ecosystem structure and functioning (Sergio et al. 2008) and

their absence can lead to significant changes in trophic cascades (Terborgh et al. 2001,

Steneck 2005, Estes et al. 2011, Ripple et al. 2014). Growing natural and anthropogenic

pressures in the form of climate change, habitat and prey depletion, poaching and human-

wildlife conflicts are pushing large carnivores into ever-shrinking habitat islands and severely

exacerbating their endangered status, and in some cases extinction (Sillero-Zubiri &

Laurenson 2001, Ceballos et al. 2005, Schipper et al. 2008, Karanth & Chellam, 2009,

Karanth et al. 2010). Recent assessments of the conservation status indicate alarming rates of

population decline for many carnivores at a global scale (Ceballos et al. 2005, Schipper et al.

2008, Karanth & Chellam, 2009, Wolf and Ripple 2017). Specifically, the families Felidae,

Canidae and Ursidae are under severe threat across the globe (Ceballos et al. 2005, Schipper

et al. 2008, Karanth et al. 2010, Wolf and Ripple 2017).

Leopard (Panthera pardus) represents the most widely distributed and adaptable member of

the family Felidae. The historical range of leopards spanned across nearly 35,000,000 km2

area covering all of sub-Saharan and North Africa, the Middle East and Asia Minor, South

and Southeast Asia, and the Russian Far East (Uphyrkina et al. 2001, Jacobson et al. 2016).

However, their current distribution and numbers have significantly decreased across the range

due to habitat loss, prey depletion, conflict and poaching over the last century (Shepherd

2001, Banks & Newman 2004, Jacobson et al. 2016). Recent meta-analyses of leopard status

and distribution suggest 48-67% range loss for the species in Africa and 83-87% in Asia

(Jacobson et al. 2016), making them among the top ten large carnivore species most-affected

by range contraction (Wolf and Ripple 2017). Out of the nine recognized leopard subspecies,

three (P. p. orientalis, P. p. nimr and P. p. melas) are classified as Critically Endangered,

while two (P. p. saxicolor and P. p. kotiya) are considered Endangered, and remaining four


https://doi.org/10.1101/746081


(P. p. japonensis, P. p. delacouri, P. p. fusca and P. p. pardus) are categorized as Near

Threatened by IUCN (Stein et al. 2016). Despite continuously decreasing numbers and range,

their ubiquitous presence across human habitations leads to misconceptions regarding their

current abundance.

Among all the subspecies, the Indian leopard (P. p. fusca) retains the largest population size

and range outside Africa (Jacobson et al. 2016). In the Indian subcontinent poaching and

conflict are major threats to leopard populations (Edgaonkar & Chellam 1998, Athreya et al.

2010, Karanth et al. 2010, 2012, 2013a, b, Raza et al. 2012, Mondol et al. 2014). Leopards

also frequently occur outside protected areas, increasing their vulnerability to conflict with

humans (Karanth et al. 2009, Athreya et al. 2010, Karanth et al. 2010, 2012a,b, 2013, Naha et

al. 2018). Unfortunately, there is still a paucity of information on their population and

demography at regional and global scales. Much of our knowledge on leopard ecology and

demography in the Indian subcontinent come from location-specific studies (Karanth &

Sunquist 2000, Chauhan et al. 2005, Harihar et al. 2009, Wang and Macdonald 2009, Kalle et

al. 2011, Grant 2012, Dutta et al. 2012a,b, Mondal et al. 2012, Borah et al. 2014, Selvan et al.

2014, Thapa et al. 2014, Pawar et al. 2019). In India, the latest estimate of leopards in the

forested habitats of 14 tiger-inhabiting states is 7910 (SE 6566-9181) (Jhala et al. 2014). As

leopards do survive in highly human populated and modified areas (Athreya et al 2013, 2014)

this estimate is likely to be minimal and incomplete. Further, recent studies in the Indian

subcontinent provide contradictory patterns of local population trends. For e.g., historical

records and occupancy estimation models based on ecological data and field observations

Karanth et al. (2010) estimated high local extinction probabilities of leopards across the

subcontinent, and Athreya et al. (2010) reported higher rates of recent conflict incidences and

related mortality at local scales. Other ecological (Harihar et al. 2011) as well as population

genetic studies of demographic history (Dutta et al. 2012a) suggest stable or increased


https://doi.org/10.1101/746081


leopard populations at local scales. However, lack of detailed, systematic field data makes it

difficult to generate accurate population estimates as well as demographic patterns at

landscape scales.

In this paper, we used non-invasively collected faecal genetic data to assess leopard genetic

variation, population structure and demographic history in the Indian subcontinent. More

specifically, we investigated (1) extent of genetic variation in leopard that persists across the

Indian subcontinent; (2) population structure of leopards at country scale; (3) the

demographic history of leopards by assessing recent changes in population size and finally

(4) compared the finding of genetic decline analyses with country wide ecological extinction

probabilities. We interpreted our results in the context of local extinction probabilities as

estimated in Karanth et al. (2010). We addressed these questions using genetic data generated

using 13 polymorphic microsatellite loci from leopard faecal samples collected across

different landscapes of India.

Methods

Research permissions and ethical considerations

All required permissions for our field surveys and biological sampling were provided by the

Forest Departments of Uttarakhand (Permit no: 90/5-6), Uttar Pradesh (Permit no: 1127/23-2-

12(G) and 1891/23-2-12) and Bihar (Permit no: Wildlife-589). Due to non-invasive nature of

sampling, no ethical clearance was required for this study.

Sampling

To detect population structure and past population demography it is important to obtain

genetic samples from different leopard habitats all across the study area. In this study, we

used leopard genetic data generated from non-invasive samples collected across the Indian

subcontinent. We conducted extensive field surveys across the Indian part of Terai-Arc

landscape (TAL) covering the north-Indian states of Uttarakhand, Uttar Pradesh and Bihar


https://doi.org/10.1101/746081


between 2016-2018. During the surveys, we opportunistically collected a total of 778 fresh

large carnivore faecal samples. These samples were collected from both inside (n=469) and

outside (n=309) protected areas from different parts of this landscape. In the field, the

samples were judged as large carnivores based on several physical characteristics such as

scrape marks, tracks, faecal diameter etc. All faecal samples were collected in wax paper and

stored individually in sterile zip-lock bags and stored inside dry, dark boxes in the field for a

maximum of two weeks period (Biswas et al. 2019). All samples were collected with GPS

locations and were transferred to the laboratory and stored in -20°C freezers until further

processing.

In addition to the north Indian samples collected in this study, we have also used leopard

genetic data previously described in Mondol et al. (2014), representing mostly the Western

Ghats and central Indian landscape. The data was earlier used in forensic analyses to assign

seized leopard samples to their potential geographic origins in India (Mondol et al. 2014).

Out of the 173 individual leopards described in the earlier study, we removed data from

related individuals and samples with insufficient data (n=30) and used remaining 143

samples for analyses in this study. These samples were collected from the states of Kerala

(n=5), Tamil Nadu (n=4), Karnataka (n=53), Andhra Pradesh (n=3), Madhya Pradesh (n=12),

Maharashtra (n=46), Gujarat (n=2), Rajasthan (n=5), Himachal Pradesh (n=8), Jharkhand

(n=1), West Bengal (n=2) and Assam (n=2), respectively. The sample locations across the

Indian subcontinent those used in the final analyses are provided in Figure 1.

DNA extraction, species and individual identification

For all field-collected faecal samples, DNA extraction was performed using protocols

described in Biswas et al. (2019). In brief, each frozen faeces was thawed to room

temperature and the upper layer was swabbed twice with Phosphate buffer saline (PBS)

saturated sterile cotton applicators (HiMedia). The swabs were lysed with 30 µl of Proteinase


https://doi.org/10.1101/746081


K (20mg/ml) and 300 µl of ATL buffer (Qiagen Inc., Germany) overnight at 56°C, followed

by Qiagen DNeasy tissue DNA kit extraction protocol. DNA was eluted twice in 100 µl

preheated 1X TE buffer. For every set of samples, extraction negatives were included to

monitor possible contaminations.

Species identification was performed using leopard-specific multiplex PCR assay described

in Mondol et al. (2014) and Maroju et al. (2016). PCR reactions were done in 10 µl volumes

containing 3.5 µl multiplex buffer mix (Qiagen Inc., Germany), 4 µM BSA, 0.2 µM primer

mix and 3 µl of scat DNA with conditions including initial denaturation (95°C for 15 min);

40 cycles of denaturation (94°C for 30 s), annealing (Ta for 30 s) and extension (72°C for 35

s); followed by a final extension (72°C for 10 min). Negative controls were included to

monitor possible contamination. Leopard faeces were identified by viewing species-specific

bands of 130 and 190 bp (Mondol et al. 2014) and 277 bp (Maroju et al. 2016) in 2% agarose

gel.

For individual identification, we used the same panel of 13 microsatellite loci previously used

in Mondol et al. (2014) (Table 1). To generate comparable data with the samples used from

earlier study by Mondol et al. (2014) we employed stringent laboratory protocols. All PCR

amplifications were performed in 10 µl volumes containing 5 µl Qiagen multiplex PCR

buffer mix (QIAGEN Inc., Germany), 0.2 µM labelled forward primer (Applied Biosystems,

USA), 0.2 µM unlabelled reverse primer, 4 µM BSA and 3 µl of the faecal DNA extract. The

reactions were performed in an ABI thermocycler with conditions including initial

denaturation (94°C for 15 min); 45 cycles of denaturation (94°C for 30 sec), annealing (Ta for

30 sec) and extension (72°C for 30 sec); followed by final extension (72°C for 30 min).

Multiple primers were multiplexed to reduce cost and save DNA (Table 1). PCR negatives

were incorporated in all reaction setups to monitor possible contamination. The PCR products

were analyzed using an automated ABI 3500XL Bioanalyzer with LIZ 500 size standard


https://doi.org/10.1101/746081


(Applied Biosystems, USA) and then alleles were scored with GENEMAPPER version 4.0

(Softgenetics Inc., USA). During data generation from field-collected samples we used one

reference sample (genotyped for all loci) from the earlier study for genotyping. As the entire

new data is generated along with the reference sample and the alleles were scored along with

the reference genotypes, the new data (allele scores) was comparable with the earlier data for

analyses.

To ensure good quality multi-locus genotypes from faecal samples, we followed a modified

multiple-tube approach in combination with quality index analyses (Miquel et al. 2006) as

described previously for leopards by Mondol et al. (2009, 2014). All faecal samples were

amplified and genotyped four independent times for all the loci. Samples producing identical

genotypes for at least three independent amplifications (or a quality index of 0.75 or more)

for each loci were considered reliable and used for all further analysis, while the rest were

discarded.

Analysis

For each locus, we calculated average amplification success as the percent positive PCR

(Broquet & Petit 2004) after four repeats across all samples. We quantified allelic dropout

and false allele rates manually as the number of dropouts or false alleles over the total

number of amplifications, respectively (Broquet & Petit, 2004), as well as using

MICROCHECKER v 2.2.3. (Oosterhout et al. 2004). The false allele frequency is calculated

for both homozygous and heterozygous genotypes as the ratio of the number of

amplifications having one or more false alleles at a particular locus and the total number of

amplifications while allele dropout rate (ADO) is calculated as the ratio between the observed

number of amplifications having loss of one allele and the number of positive amplifications

of the heterozygous individuals.


https://doi.org/10.1101/746081


Post data quality assessment and finalization of consensus genotypes for all samples we

selected only those samples with good quality data for at least nine or more loci (out of 13)

for further analyses. We used the identity analysis module implemented in program CERVUS

(Kalinowski, Taper & Marshall, 2007) to identify identical genotypes (or recaptures) by

comparing data from all samples. All genetic recaptures were removed from the data set.

GIMLET (Valiere, 2002) was used to calculate the PID(sibs) for all the unique individuals.

Following this, any allele having less than 10% frequency across all amplified samples were

rechecked for allele confirmation. ARLEQUIN (Excoffier, Laval & Schneider, 2005) was

used to determine Hardy Weinberg equilibrium and linkage disequilibrium for all the loci.

Finally, to avoid the effects of related individuals in all analyses, we used program

GENECLASS 2.0 (Piry et al. 2004) to select out related individuals in our samples.

To determine genetic structure of leopards across the Indian subcontinent we used a Bayesian

clustering approach implemented in program STRUCTURE (Pritchard et al. 2000, Falush et

al. 2003). We performed 10 independent analyses for each K values between one and ten,

using 450,000 iterations and a burn-in of 50,000 assuming correlated allele frequencies. The

optimal value of K was determined using STRUCTURE HARVESTER web version (Earl &

vonHoldt, 2012). Subsequent summary statistics were calculated in ARLEQUIN 3.1

(Excoffier et al. 2005) and indices of overall genetic differentiation (pairwise Fst) were

estimated using GenAlEx version 6.5 (Peakall and Smouse 2012), dividing the leopard

populations according to the STRUCTURE results across the Indian subcontinent. The

divisions were based on Q-values (estimated proportions of ancestry) calculated in

STRUCTURE, where we used Q> 0.75 as threshold for assigning individuals to a particular

population (Mora et al. 2010). Additionally compression of expected heterozygosity (or Gst)

(Nei, 1987) between four leopard sub-populations was calculated in GenAlEx version 6.5

(Peakall and Smouse 2012).


https://doi.org/10.1101/746081


Demography analyses

Demographic analyses were performed with different genetic subpopulations of leopards

based on the results from STRUCTURE analyses. We used a number of different approaches

to detect past population demography for leopards. The first two qualitative approaches use

summary statistics to detect population size changes, whereas the quantitative approach is a

likelihood-based Bayesian algorithm. The summary statistic-based methods used were the

Ewens, Watterson, Cornuet and Luikart method implemented in program BOTTLENECK

(Cournet & Luikart 1996), and the Garza-Williamson index or M ratio (Garza & Williamson

2001) implemented in program ARLEQUIN 3.1. The quantitative Bayesian approach used

was implemented in the program MSVAR 1.3 (Storz & Beaumont 2002).

a) The Ewens, Watterson, Cornuet and Luikart (EWCL) approach:

This approach allows the detection of population size changes using two summary statistics

of the allele frequency spectrum, number of alleles (NA) and expected heterozygosity (He)

across different mutational models. Simulations are performed to obtain the expected

distribution of He for a demographically stable population under three mutation models:

infinite allele model (IAM), single stepwise model (SMM) and two-phase model (TPM) and

the values are then compared to the real data values. This method can detect departures from

mutation-drift equilibrium and neutrality, which can be explained by any departure from the

null model, including selection, population growth or decline. More importantly, consistent

results from independent loci could be attributed to demographic events over selection. For

simulations with TPM model, we used two different (5% and 30%) multi-step mutation

events for leopards.

b) The Garza-Williamson index/M ratio approach:

This approach allows the detection of population decline using two summary statistics of the

allele frequency spectrum, number of alleles (NA) and the allelic size range. The basic


https://doi.org/10.1101/746081


principle behind this approach is in a reducing population, the expectation of the reduction of

number of alleles is much higher than the reduction of allelic size range. Thus, the ratio

between the number of alleles and the allelic size range is expected to be smaller in recently

reduced populations than in equilibrium populations.

c) The Storz and Beaumont approach:

This approach is an extension of Beaumont’s approach (Beaumont 1999) that assumes a

stable population of size N1 started to change (either decrease or increase) Ta generations ago

to the current population size N0. This change in the population size is assumed to be at an

exponential scale under stepwise mutation model (SMM), at a rate y=2N0m, where m is the

mutation rate per locus per generation. This Bayesian approach uses the information from the

full allelic distribution in a coalescent framework to estimate the posterior probability

distribution, allowing quantification of effective population sizes N0 and N1, rather than their

ratio (as in Beaumont 1999) along with T, time since the population change. In this approach,

prior distributions for N0, N1, T and µ (mutation rate) are assumed to be log normal. The

mean and the standard deviations of these prior log normal distributions are drawn from prior

(or hyperpriors) distributions. A Markov Chain Monte Carlo (MCMC) algorithm is used to

generate samples from the posterior distribution of these parameters. We used wide

uninformative priors to perform multiple runs for this approach (Supplementary Table 1). For

minimal effect towards the posterior distributions variances for the prior distributions were

kept large. A total number of 2 million iterations were performed for each run.

The generation time for leopards are known to be about 4-5 years (Dutta et al. 2012b) and we

used a five-year generation time for all analyses.

Estimation of leopard extinction probability

To understand extinction probability across various biogeographic zones of India we

analysed patterns and determinants of leopard occurrence as described in Karanth et al.


https://doi.org/10.1101/746081


(2009, 2010). We applied a grid-based approach to determine current distribution patterns for

leopards, where the selection of grids was based on prior information of leopard presence.

This involved collating presence-absence information from more than 100 Indian wildlife

experts along with historical information of leopard presence involving hunting locations and

other taxidermy and museum records. Each grid cell was an average of 2818 km2 in size and

we used data from 1229 grid cells covering 3,46,3322 km2 area of the Indian subcontinent.

This study applied occupancy modelling to examine the influence of ecological and social

covariates on patterns of leopard occupancy. We used a maximum likelihood approach for

leopard occupancy in PRESENCE. V.2.0 program (Hines 2006). Covariates likely to

influence leopard distribution modelled included presence and extent of protected areas, land

cover-land use characteristics, human cultural tolerance and population density. Data for

protected areas was retrieved from the World Database on protected areas (www.unep-

wcmc.org) and topographic maps. Land cover- land use data were derived from Global Land

Cover Facility (2000) and further refined based on Roy et al. (2006) and Joshi et al. (2006). A

human tolerance index that characterized different Indian states from most to least tolerant

was developed based on knowledge about society-culture, law enforcement, hunting patterns

and prior field experiences (for details see Karanth et al. 2009, 2010). Human population

density data were derived from LandScan Global Population Data 2000 (www.ornl.gov/gist).

Based on existing information on species’ ecology we predicted higher occupancy in

protected areas, deciduous-grass-scrub land cover types and lower occupancy in less tolerant

states and highly populated areas because of direct completion for food and space

(Rangarajan 2001). We performed pair-wise correlation tests to screen variables for

multicollinearity. The occupancy approach accounts for non-detection of species during

surveys and inability to survey some sites (see Karanth et al. 2009, 2010 for additional

details). The probability of extinction was calculated as (1- probability of occurrence). We


https://doi.org/10.1101/746081


derived leopard extinction probabilities for three separate major landscapes (Western Ghats,

Central India and North India) as these regions strongly represented our genetic sampling.

These extinction probabilities were compared to the genetically derived estimates.

Results

Individual identification of leopards from north Indian landscape

Of the 778 large carnivore faecal samples collected from TAL, we identified 195 faeces to be

of leopard origin (25%) using species-specific PCR assays (Mondol et al. 2014; Maroju et al.

2016). In addition, 457 samples were ascertained to be of tiger (59%) and remaining 126

faecal samples did not produce any result (16%). We amplified 13 microsatellite loci panel

on these 195 genetically confirmed leopard faecal samples, and after data validation through

multiple repeats generated seven or more loci data from 65 faecal DNA. Subsequently, we

identified 56 unique leopard individuals from the 65 samples, whereas nine individuals were

ascertained as ‘genetic recaptures’. The mean allelic dropout rate for these loci was found to

be 0.05, whereas mean false allele rate for all the 13 loci was 0.04., indicating this 13 loci

panel has low genotyping error rates. Amplification success ranged between 41% to 100%

from leopard faecal DNA. None of the loci were found to deviate from the Hardy-Weinberg

equilibrium and there were no evidences for strong linkage disequilibrium between any pair

of loci. Cumulative PIDsibs and PIDunbiased values were found to be 3.91*10-6 and 2.73*10-16,

respectively, indicating a strong statistical support for unambiguous individual identification.

Summary statistics for these samples collected across Terai-Arc landscape is provided in

Table 1. We identified 26, 21 and nine unique leopard individuals from the states of

Uttarakhand, Uttar Pradesh and Bihar, respectively. As the data generated from north India is

comparable to the earlier data, we added this 56 unique leopard data to 143 individual

genotypes described in Mondol et al. (2014), and overall 199 unique unrelated leopards were

used in subsequent population structure, genetic variation and demography analyses.


https://doi.org/10.1101/746081


Leopard population structure and genetic variation across India

Our sampling strategy targeted country-wise leopard populations to assess population

structure and genetic variation. From 199 final unique leopard genotypes we removed four

samples representing the eastern and northeast India (n=2 from the states of West Bengal and

Assam each, respectively) from further analyses as they represented inadequate sampling

from these regions. Genetic clustering analysis using 13 microsatellite data from the

remaining 195 wild leopard individuals showed four distinct genetic subpopulations (K=4,

see Supplementary Figure 1), as presented in Figure 1. Majority of the samples showed

respective group-specific ancestry, with Western Ghats samples representing the first group

(henceforth WG, n=65), the Deccan Plateau-Semi Arid region forming the second cluster

(henceforth DP-SA, n=66), the samples from Shivalik region covering parts of Himalaya and

western parts of upper Gangetic plains making the third group (henceforth SR, n=38), and

finally samples from the Terai region covering eastern part of upper and western part of the

lower Gangetic Plains samples forming the fourth cluster (henceforth TR, n=26), respectively

(Figure 1). However, small number of samples (n=18) distributed among the four

subpopulations showed mixed ancestry. Subsequent analyses revealed that these leopard

subpopulations are genetically differentiated (Fst and Gst) at low, but significant levels (Table

2) for all four populations. The Fst value among these populations ranged between 0.028-

0.115, whereas the Gst value between 0.023-0.104 (Table 2).

Analyses and with 13 microsatellite loci among the four genetic subpopulations showed a

higher mean number of alleles (NAWG= 11.77 (S.D. 3.85), NADP-SA=10.46 (S.D. 2.71)) and

observed heterozygosity (HoWG=0.81 (S.D. 0.08), HoDP-SA=0.8 (S.D. 0.08)) in Western Ghats

and Deccan Plateau-Semi Arid subpopulations, when compared with samples from Shivalik

and Terai region subpopulations (NASR=08.46 (S.D. 2.41), NATR=05.00 (S.D. 1.84) and


https://doi.org/10.1101/746081


HoSR=0.40 (S.D. 0.14), HoTR=0.36 (S.D. 0.28), respectively) (see Table 3 for details).

However, the allelic size range values were similar in all populations (Table 3).

Detection of demographic change

We used the microsatellite data to investigate signals of demographic changes in each of the

four leopard genetic subpopulations across the subcontinent. Both of the qualitative

approaches, the EWCL and the M-ratio methods indicate signatures of population bottleneck.

The EWCL approach implemented in the program BOTTLENECK shows 8-10 loci with

heterozygote excess depending on the mutation models used, suggesting a loss of rare alleles

through population decline for all four subpopulations. Similarly the M-ratio approach also

shows a low ratio between number of alleles (NA) and the allelic size range in all four

populations (M-ratioWG- 0.37 (S.D. 0.09); M-ratioDP-SA- 0.38 (S.D. 0.09); M-ratioSR- 0.33

(S.D. 0.09); M-ratioTR- 0.29 (S.D. 0.15)), indicating signatures of population bottleneck.

However, both of these approaches cannot quantify the extent and timing of the bottleneck

events. We used the Storz and Beaumont approach for quantification and dating of any such

events through coalescent simulations. Models with exponential decline scenarios show

consistently that the posterior distributions for log (N0) is always lower than log (N1) for all

four subpopulations, indicating population decline for leopards across the subcontinent

(Table 4 and Figure 2). Further quantification revealed that the current effective size is

varyingly low (12-25%) than the historical effective size, with Western Ghats, Deccan

Plateau-Semi Arid, Shivalik and Terai regions losing approximately 75%, 90%, 90% and

88% of their leopard population, respectively (Table 4 and Figure 2).

This approach also allowed us to date the population collapse by providing a posterior

distribution for the time at which the decline started. Our analyses revealed distributions that

suggested recent time of declines in all four populations of leopards (Table 4, Figure 2). The

north Indian subpopulations (Shivalik and Terai) and the Deccan Plateau-Semi Arid


https://doi.org/10.1101/746081


population showed the most recent decline occurred about 120-125 years before present,

respectively. However, the Western Ghats population indicated potential decline around 200

years ago (Table 4 and Figure 2).

Leopard occurrence and distribution

We examined the factors influencing leopard distribution at a countrywide scale, where the

top ranked model incorporating 28 covariates suggested a wide distribution of habitat types

(described in Karanth et al. 2009, 2010). The model also indicated a positive influence of

protected areas and negative influence of higher human population densities and higher

cultural tolerance of people (details in Karanth et al. 2009). Areas with cultivated land, barren

areas, deciduous forests and rural-urban were strongly associated with higher leopard

occurrence. Naive estimated occupancy was 0.52, whereas model estimated probability of

occupancy was significantly higher at 0.68, suggesting that leopards are still widely

distributed (Figure 3) in India compared to most other large mammals (as suggested in

Karanth et al. (2010)). When compared among the overall three major sub-regions (North

India (NI), Deccan Plateau-Semi Arid and Western Ghats), we find that average estimated

occurrence was lowest in the North India (PsiNI=0.63, Range: 0.05-1.00, Standard error=0.01,

Number of cells=384) compared to Western Ghats (PsiWG=0.83, Range: 0.23-1.00, Standard

error=0.02, Number of cells=90) and Deccan Plateau-Semi Arid (PsiDP-SA=0.79, Range:0.25-

1.00, Standard error=0.005, Number of cells=818). Overall, average estimated Psi was 0.74

(Standard error=0.006, Number of cells=1292).

Discussion

To the best of our knowledge, this is probably the first and most exhaustive country level

sampling based study on leopard population genetics and demographic patterns in the Indian

subcontinent. Except the eastern and northeast Indian landscape, where our sampling

intensity was less all other regions are well covered in this study. Our genetic analyses with


https://doi.org/10.1101/746081


microsatellite data collected across the subcontinent reveal four genetic subpopulations of

leopards in India: the Western Ghats, Deccan Plateau-Semi Arid landscape, hill region of

north India (Shivalik) and Terai or flat region of north India. While there was some amount

of mixed genetic signal across different genetic subpopulations, they were clearly separated

as different clusters (Figure 1). These genetic clusters mostly correspond to respective

biogeographic zones of India, with Western Ghats and combination of Deccan Plateau-Semi

Arid forms two clusters, whereas the north Indian clusters of Shivalik and Terai are parts of

the Himalayan and Gangetic Plains zones, respectively. It is possible that these genetic

clusters were formed due to the species distribution across various habitat types in different

biogeographic zones across Indian subcontinent. For example, earlier study has reported

difference in leopard occupancy in ‘Bhabar’ habitats of Shivalik (high abundance) and flat

Terai region (low abundance) due to presence of socially dominant tigers and the absence of

rugged escape terrain for leopards (Johnsingh et al. 2004). Such habitat heterogeneities might

have resulted in developing genetic differences across these landscapes.

Overall, these four subpopulations were genetically differentiated by low, but significant Fst

and Gst values across all comparisons (Fst and Gst values ranging from 0.028-0.115 and 0.023-

0.104, respectively, see Table 2). Previous studies on tigers (Mondol et al. 2009, 2013,

Kolipakam et al. 2019, subcontinent scale) as well as leopards (Dutta et al. 2012b, central

Indian landscape) suggested long-distance movement as a potential cause for low genetic

differentiation between populations. Leopards are more widely distributed than other

sympatric large carnivores (tiger, lion) in the Indian subcontinent due to their general

adaptability and wider diet spectrum (Seidensticker et al. 1990, Edgaonkar & Chellam 1998,

Athreya et al. 2010), and disperse long distances (Sunquist 1983, Bailey 1993) often through

human habitats. In addition, human-leopard conflict driven translocation is common in many

parts in India (Athreya et al. 2010). Together, natural dispersal abilities and ‘human mediated


https://doi.org/10.1101/746081


gene flow’ because of translocation might be responsible for the low genetic differentiation

among leopard subpopulations across the subcontinent. Earlier work in central Indian

landscape (Dutta et al. 2012b) suggested a reduction in gene flow at recent times due to

habitat destruction, but our study did not focus to answer such questions. Future studies

should focus on using historical samples (e.g. museum skins, bones etc.) to assess any

possible change in gene flow among leopard populations (For e.g. see Martinez-Cruz et al.

2007, Valdiosera et al. 2008, Lorenzen et al. 2011, Mondol et al. 2013) at subpopulation

levels across the country.

However, our demography analyses with genetic data indicate strong decline in leopard

population size across all four genetic subpopulations. Results with both qualitative

(bottleneck and M-ratio approach) as well as quantitative (Storz and Beaumont approach)

analyses revealed strong, but varying signals of population decline in all four subpopulations

(Table 4). The Deccan Plateau-Semi Arid, Shivalik and Terai subpopulations show 90%,

90% and 88% decline in population size, respectively, whereas the Western Ghats population

show relatively less (75%) decline in population size (Table 4). This pattern is possible as the

Western Ghats still retains possibly the largest contiguous forested landscape with multiple

interconnected protected area landscape, whereas the other regions have lot of human

activities, possibly affecting leopard populations living in them. Further, the ecological data

based occupancy analysis showed extinction probabilities of 0.37, 0.21 and 0.17 for North

India, Deccan Plateau-Semi Arid and Western Ghats landscape, respectively (Table 4). This

is not surprising as throughout their distribution leopards are closely associated with human

population, making them vulnerable to conflict and poaching (Gavashelishvili & Lukarevskiy

2008, Athreya et al. 2010, Balme et al. 2010). While there is a discrepancy between the

magnitudes of decline based on genetic and ecological models, it is possible that such pattern

is because the ecological methods are more spatial, and inference is based on how much area


https://doi.org/10.1101/746081


leopards occupied in the past and how this has changed. However, if densities of leopards

were high in the past, loss of even small habitats could result in the loss of many individuals.

Since no quantitative comparisons for leopard density between the Western Ghats, Deccan

Plateau-Semi Arid and North India is currently available, we cannot conclusively infer the

former, but further research should investigate leopard densities and their temporal changes

across the country. Finally, this decline pattern also roughly corroborates with 83-87%

leopard range loss in Asia, indicating that habitat loss is possibly leading to population

decline.

The magnitude of decline for leopards found in this study is contrasting to some of the earlier

leopard studies in the subcontinent (for e.g. ecological work by Harihar et al. 2011, and

genetic work by Dutta et al. 2012a) and Africa (Spong et al. 2000b), which suggest stable or

increasing local leopard population trends. This is certainly possible as many of these studies

were conducted inside protected areas, where leopard population dynamics depends on

presence/absence of other large carnivores (tiger, dhole etc.) and other ecological factors.

However, only 11% of Indian leopard distribution is within protected area network (Jacobson

et al 2016), and our sampling at subcontinent scale is thus probably indicating the decline

patterns at much larger scale. Nevertheless, this pattern of population decline is consistent

with many other endangered species in the Asian region (for e.g. tiger-98% decline, Mondol

et al. 2009; giant panda-90% decline, Zhu et al. 2010; orangutan-95% decline, Goossens et al.

2006; red panda-98% decline, Hu et al. 2011; Prezwalski’s gazelle-99% decline, Yang and

Jiang 2011 etc.) as well as top carnivore species across the globe (for e.g. Finnish wolf-92%

decline, Aspi et al. 2006; Otter-75% decline, Hajkova et al. 2007; Golden eagle-94% decline,

Bourke et al. 2010; African wild dog-70% decline, Marsden et al. 2012; Fisher-90% decline,

Tucker et al. 2012 etc.).


https://doi.org/10.1101/746081


Another important finding is the relatively recent timing of decline for all the leopard

subpopulations in the subcontinent. Our results suggest median leopard decline timing

between 120-200 years across four genetic subpopulations (Table 4). Except Western Ghats

(decline timing of ∼200 years), all other subpopulations indicate much recent population

decline (Central India-Deccan Plateau ∼125 years, Shivalik ∼120 years and Terai ∼125

years). When compared with other sympatric, endangered species in the subcontinent (for e.g.

tiger decline ∼200 years ago; Mondol et al. 2009) or in the region (for e.g. Orangutan- ∼210

years, Goossens et al. 2006; Giant panda- ∼250 years, Zhu et al. 2010) this still seems to be

much recent event. Other wide-ranging carnivores across the globe (for e.g. European wolves

Aspi et al. 2006; African wild dog- Marsden et al. 2012; Eurasian badgers- Franz et al. 2014

etc.) too faced much longer decline period than leopards. One plausible explanation could be

recent increase in leopard-human conflict (Athreya et al. 2010) and poaching intensity due to

large demand of leopard body parts in the illegal wildlife markets (Raza et al. 2012; WPSI

2014). Historically, major leopard hunting events had been recorded across the Indian

subcontinent during Mughal times (about 500-600 years ago), followed by colonial British

bounty-hunting rule between 1850-1920 (Rangarajan 2006). However, large-scale landscape

modification and fragmentation by human during the last century (central India- Rangarajan

1999, north India- Rangarajan 2006), coupled with poaching and conflict has possibly

resulted in much recent loss of leopard populations across the country. Apart from sporadic

information, we lack comprehensive data, both at historical as well as modern scales to

investigate the true causes behind such patterns of differential population decline timing. For

e.g., Dutta et al. (2012b) showed that during last three centuries severe changes in landscape

characteristics (Settlement, villages, wild lands, human density) have occurred in the central

Indian leopard habitats. However, we lack information on hunting and conflict levels from

these regions. Future efforts should generate this important information to get an idea of the


https://doi.org/10.1101/746081


scenarios leading to such strong decline in a wide-ranging species like leopard. Finally, it is

important to point out that in this study we have only explored relatively simple decline

scenarios, and future studies should evaluate more detailed, computationally intensive

demographic analyses with genome wide molecular markers (For e.g. see Franz et al. 2014,

Nater et al. 2015) for better understanding of complex decline scenarios.

Another important aspect of the results from this study is that despite severe decline (Table 4)

and small, but significant population structure (Figure 1B, Table 2) leopards still retain high

genetic variation in the Indian subcontinent. We found that leopard genetic variation across

four genetic subpopulations is similar and comparable to Africa (Spong et al. 2000b,

Uphyrkina et al. 2001), and higher than Arabian (Ilani 1981, Perez et al. 2006) and Amur

leopards (Uphyrkina et al. 2001, 2002, Sugimoto et al. 2014). The higher levels of variation

could possibly be attributed to still relatively large population size, high pre-bottleneck

genetic variation and potential historical gene flow across large landscapes.

Conclusion

While leopards are relatively easier to study than other sympatric carnivores like tigers due to

their ubiquitous presence, studies on their ecology are limited. In fact, due to their broad

geographic distribution, leopard populations are perceived to be stable, with current IUCN

Red List status of ‘vulnerable’. However, both historical records and recent conflict with

humans suggest potentially declining population trends. Using genetic data, we reveal a

strong signal of population decline (between 75-90%) across different habitats in the Indian

subcontinent over the last 120-200 years. Our results are interesting because we demonstrate

population decline in a wide-ranging and, commonly perceived as locally abundant species

like the leopard, suggesting that leopards demand similar conservation attention like tigers in

India. While we are unable to corroborate these population decline patterns with leopard

census data, our results suggest that it will be important to generate such ecological


https://doi.org/10.1101/746081


abundance estimates for leopard populations in the near future. This work also emphasizes

the importance of similar work on wide-ranging species, as it is possible that other species

like the leopard may show population declines, especially in the context of the Anthropocene.

Acknowledgement:

We acknowledge the Director, Dean and Nodal Officer of Wildlife Forensics and

Conservation Genetics Cell of Wildlife Institute of India for their support in this work. Our

sincere thanks to Forest Departments of Uttarakhand, Uttar Pradesh and Bihar for research

permits. We thank Dr. Uma Ramakrishnan of National Centre for Biological Sciences for

providing reference leopard samples. Mr A. Madhanraj has provided critical support in

genotyping facility and Mr. Debanjan Sarkar helped with GIS in the laboratory. We thank all

the lab members of Wildlife Forensic and Conservation Genetics cell and especially Meercat

lab for productive discussions and valuable comments. We also thank Dr. S. K. Gupta and

Dr. S. P. Goyal for logistic support; our field assistants Annu, Bura, Abbhi, Ranjhu and Imam

for their effort in the field. This research was funded by Wildlife Conservation Trust-Panthera

Global Cat Alliance Grants and Department of Science and Technology, Government of

India grant no EMR/2014/000982. Samrat Mondol was supported by the Department of

Science and Technology INSPIRE Faculty Award (No.IFA12-LSBM-47).

References:

Aspi J, Roininen E, Ruokonen M, Kojola I, Vila C. 2006. Genetic diversity, population

structure, effective population size and demographic history of the Finnish wolf

population. Molecular Ecology 15:1561–1576. DOI: 10.1111/j.1365-

294X.2006.02877.x.

Athreya V, Odden M, Linnell JDC, Karanth KU. 2010. Translocation as a Tool for

Mitigating Conflict with Leopards in Human-Dominated Landscapes of India.

Conservation Biology 25:133–141. DOI: 10.1111/j.1523-1739.2010.01599.x.


https://doi.org/10.1101/746081


Balme GA, Slotow R, Hunter LTB. 2009. Impact of conservation interventions on the

dynamics and persistence of a persecuted leopard (Panthera pardus) population.

Biological Conservation 142:2681–2690. DOI: 10.1016/j.biocon.2009.06.020.

Banks D, Newman J. 2004. The Tiger Skin Trail. :21.

Beaumont MA. 1999. Detecting population expansion and decline using microsatellites.

Genetics 153:2013–2029.

Biswas S, Bhatt S, Paul S, Modi S, Ghosh T, Habib B, Nigam P, Talukdar G, Pandav B,

Mondol S. 2019. A practive faeces collection protocol for multidisciplinary research in

wildlife science. Current Science 116:1878–1885. DOI: 10.18520/cs/v116/i11/1878-

1885.

Borah J, Sharma T, Das D, Rabha N, Kakati N, Basumatary A, Ahmed MF, Vattakaven J.

2014. Abundance and density estimates for common leopard Panthera pardus and

clouded leopard Neofelis nebulosa in Manas National Park, Assam, India. Oryx 48:149–

155. DOI: 10.1017/S0030605312000373.

Bourke BP, Frantz AC, Lavers CP, Davison A, Dawson DA, Burke TA. 2010. Genetic

signatures of population change in the British golden eagle (Aquila chrysaetos).

Conservation Genetics 11:1837–1846. DOI: 10.1007/s10592-010-0076-x.

Broquet T, Petit E. 2004. Quantifying genotyping errors in noninvasive population genetics.

Molecular Ecology 13:3601–3608. DOI: 10.1111/j.1365-294X.2004.02352.x.

Ceballos G, Ehrlich PR, Sobero J, Salazar I, Fay JP. 2005. Global Mammal Conservation:

What Must We Manage? Science 309:603–607. DOI: 10.1126/science.1110063.

Dutta T, Sharma S, Maldonado JE, Wood TC, Panwar HS, Seidensticker J. 2013. Gene flow

and demographic history of leopards ( Panthera pardus ) in the central Indian highlands.

Evolutionary Applications 6:949–959. DOI: 10.1111/eva.12078.

Dutta T, Sharma S, Maldonado JE, Wood TC, Seidensticker J. 2012. A reliable method for


https://doi.org/10.1101/746081


individual identification and gender determination of wild leopards (Panthera pardus

fusca) using non-invasive samples. Conservation Genetics Resources 4:665–667. DOI:

10.1007/s12686-012-9618-5.

Earl DA, vonHoldt BM. 2012. STRUCTURE HARVESTER: A website and program for

visualizing STRUCTURE output and implementing the Evanno method. Conservation

Genetics Resources 4:359–361. DOI: 10.1007/s12686-011-9548-7.

Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, Carpenter SR, Essington

TE, Holt RD, Jackson JBC, Marquis RJ, Oksanen L, Oksanen T, Paine RT, Pikitch EK,

Ripple WJ, Sandin SA, Scheffer M, Schoener TW, Shurin JB, Sinclair ARE, Soulé ME,

Virtanen R, Wardle DA. 2011. Trophic Downgrading of Planet Earth. Science 333:301–

306. DOI: 10.1126/science.1205106.

Excoffier L, Laval G, Schneider S. 2005. Arlequin ver 3.1: An Integrated Software Package

for Population Genetics Data Analysis. DOI: 10.1093/nq/s9-II.43.326-b.

Falush D, Stephens M, Prithard JK. 2003. Inference of Population Structure Using Multilocus

Genotype Data: Linked Loci and Correlated Allele Frequencies Daniel. JAMA: The

Journal of the American Medical Association 164:1567–1587. DOI:

10.1001/jama.1987.03400040069013.

Garza JC, Williamson EG. 2001. Detection of reduction in population size using data from

microsatellite loci. Molecular Ecology 10:305–318.

Gavashelishvili A, Lukarevskiy V. 2008. Modelling the habitat requirements of leopard

Panthera pardus in west and central Asia. Journal of Applied Ecology 45:579–588. DOI:

10.1111/j.1365-2664.2007.01432.x.

Goossens B, Chikhi L, Ancrenaz M, Lackman-Ancrenaz I, Andau P, Bruford MW. 2006.

Genetic Signature of Anthropogenic Population Collapse in Orang-utans. PLoS Biology

4:e25. DOI: 10.1371/journal.pbio.0040025.


https://doi.org/10.1101/746081


Grant T. 2012. Leopard population density, home range size and movement patterns in a

mixed landuse area of the mangwe district of Zimbabwe. :1–143.

Hájková P, Pertoldi C, Zemanová B, Roche K, Hájek B, Bryja J, Zima J. 2007. Genetic

structure and evidence for recent population decline in Eurasian otter populations in the

Czech and Slovak Republics: implications for conservation. Journal of Zoology 272:1–

9. DOI: 10.1111/j.1469-7998.2006.00259.x.

Harihar A, Pandav B, Goyal SP. 2009. Responses of tiger (Panthera tigris) and their prey to

removal of anthropogenic influences in Rajaji National Park, India. European Journal of

Wildlife Research 55:97–105. DOI: 10.1007/s10344-008-0219-2.

Harihar A, Pandav B, Goyal SP. 2011. Responses of leopard Panthera pardus to the recovery

of a tiger Panthera tigris population. Journal of Applied Ecology 48:806–814. DOI:

10.1111/j.1365-2664.2011.01981.x.

Hines, J. E. (2006). Program presence. See http://www. mbrpwrc. usgs.

gov/software/doc/presence/presence. html.

Hu Y, Guo Y, Qi D, Zhan X, Wu H, Bruford MW, Wei F. 2011. Genetic structuring and

recent demographic history of red pandas (Ailurus fulgens) inferred from microsatellite

and mitochondrial DNA. Molecular Ecology 20:2662–2675. DOI: 10.1111/j.1365-

294X.2011.05126.x.

Jacobson AP, Gerngross P, Lemeris Jr. JR, Schoonover RF, Anco C, Breitenmoser-Würsten

C, Durant SM, Farhadinia MS, Henschel P, Kamler JF, Laguardia A, Rostro-García S,

Stein AB, Dollar L. 2016. Leopard ( Panthera pardus ) status, distribution, and the

research efforts across its range. PeerJ 4:e1974. DOI: 10.7717/peerj.1974.

Jhala YV, Qureshi Q, Gopal R. 2015. The status of tigers in India, 2014.

Johnsingh AJT, Ramesh K, Qureshi Q, A. D, Goyal SP, Rawat GS, Rajapandian K, Prasad S.

2004. Conservation Status of Tiger and Associated Species in the Terai Arc Landscape ,


https://doi.org/10.1101/746081


India.

Kalinowski ST, Taper ML, Marshall TC. 2007. Revising how the computer program cervus

accommodates genotyping error increases success in paternity assignment. Molecular

Ecology 16:1099–1106. DOI: 10.1111/j.1365-294X.2007.03089.x.

Kalle R, Ramesh T, Qureshi Q, Sankar K. 2011. Density of tiger and leopard in a tropical

deciduous forest of Mudumalai Tiger Reserve, southern India, as estimated using

photographic capture–recapture sampling. Acta Theriologica 56:335–342. DOI:

10.1007/s13364-011-0038-9.

Karanth KU, Chellam R. 2009. Carnivore conservation at the crossroads. Oryx 43:1. DOI:

10.1017/S003060530843106X.

Karanth KK, Gopalaswamy AM, DeFries R, Ballal N. 2012. Assessing Patterns of Human-

Wildlife Conflicts and Compensation around a Central Indian Protected Area. PLoS

ONE 7:e50433. DOI: 10.1371/journal.pone.0050433.

Karanth KK, Naughton-Treves L, DeFries R, Gopalaswamy AM. 2013. Living with Wildlife

and Mitigating Conflicts Around Three Indian Protected Areas. Environmental

Management 52:1320–1332. DOI: 10.1007/s00267-013-0162-1.

Karanth KK, Nichols JD, Hines JE, Karanth KU, Christensen NL. 2009. Patterns and

determinants of mammal species occurrence in India. Journal of Applied Ecology

46:1189–1200. DOI: 10.1111/j.1365-2664.2009.01710.x.

Karanth KK, Nichols JD, Karanth KU, Hines JE, Christensen NL. 2010. The shrinking ark:

patterns of large mammal extinctions in India. Proceedings of the Royal Society B:

Biological Sciences 277:1971–1979. DOI: 10.1098/rspb.2010.0171.

Karanth KU, Sunquist M. 2000. Behavioural Correlates of Predation by Tiger ( Panthera

tigris ) & Leopard ( Panthera pardus ) in. The zoological society of London 4:255–265.

Lorenzen ED, Nogués-Bravo D, Orlando L, Weinstock J, Binladen J, Marske KA, Ugan A,


https://doi.org/10.1101/746081


Borregaard MK, Gilbert MTP, Nielsen R, Ho SYW, Goebel T, Graf KE, Byers D,

Stenderup JT, Rasmussen M, Campos PF, Leonard JA, Koepfli K-P, Froese D, Zazula

G, Stafford TW, Aaris-Sørensen K, Batra P, Haywood AM, Singarayer JS, Valdes PJ,

Boeskorov G, Burns JA, Davydov SP, Haile J, Jenkins DL, Kosintsev P, Kuznetsova T,

Lai X, Martin LD, McDonald HG, Mol D, Meldgaard M, Munch K, Stephan E, Sablin

M, Sommer RS, Sipko T, Scott E, Suchard MA, Tikhonov A, Willerslev R, Wayne RK,

Cooper A, Hofreiter M, Sher A, Shapiro B, Rahbek C, Willerslev E. 2011. Species-

specific responses of Late Quaternary megafauna to climate and humans. Nature

479:359–364. DOI: 10.1038/nature10574.

Marie J, Luikart G. 1996. Power Analysis of Two Tests for Detecting Recent Population

Bottlenecks From Allele Frequency Data. Test 144:2001–2014. DOI: Article.

Maroju PA, Yadav S, Kolipakam V, Singh S, Qureshi Q, Jhala Y. 2016. Schrodinger’s scat: a

critical review of the currently available tiger (Panthera Tigris) and leopard (Panthera

pardus) specific primers in India, and a novel leopard specific primer. BMC Genetics

17:37. DOI: 10.1186/s12863-016-0344-y.

Marsden CD, Woodroffe R, Mills MGL, Mcnutt JW, Creel S, Groom R, Emmanuel M,

Cleaveland S, Kat P, Rasmussen GSA, Ginsberg J, Lines R, Andre J-M, Begg C, Wayne

RK, Mable BK. 2012. Spatial and temporal patterns of neutral and adaptive genetic

variation in the endangered African wild dog (Lycaon pictus). Molecular Ecology

21:1379–1393. DOI: 10.1111/j.1365-294X.2012.05477.x.

Martinez-Cruz B, Godoy JA, Negro JJ. 2007. Population fragmentation leads to spatial and

temporal genetic structure in the endangered Spanish imperial eagle. Molecular Ecology

16:477–486. DOI: 10.1111/j.1365-294X.2007.03147.x.

Miquel C, Bellemain E, Poillot C, Bessière J, Durand A, Taberlet P. 2006. Quality indexes to

assess the reliability of genotypes in studies using noninvasive sampling and multiple-


https://doi.org/10.1101/746081


tube approach. Molecular Ecology Notes 6:985–988. DOI: 10.1111/j.1471-

8286.2006.01413.x.

Mondal K, Sandar K, Qureshi Q, Gupta S, Chourasia P. 2012. Estimation of population and

survivorship of leopard through photographic cature-recapture sampling in west india.

World Journal of Zoology 7:30–39. DOI: 10.5829/idosi.wjz.2012.7.

Mondol S, Bruford MW, Ramakrishnan U. 2013. Demographic loss, genetic structure and the

conservation implications for Indian tigers. Proceedings of the Royal Society B:

Biological Sciences 280:20130496–20130496. DOI: 10.1098/rspb.2013.0496.

Mondol S, Karanth KU, Ramakrishnan U. 2009. Why the Indian Subcontinent Holds the Key

to Global Tiger Recovery. PLoS Genetics 5:e1000585. DOI:

10.1371/journal.pgen.1000585.

Mondol S, Kumar NS, Gopalaswamy A, Sunagar K, Karanth KU, Ramakrishnan U. 2014.

Identifying species, sex and individual tigers and leopards in the Malenad-Mysore Tiger

Landscape, Western Ghats, India. Conservation Genetics Resources 7:353–361. DOI:

10.1007/s12686-014-0371-9.

Mondol S, R N, Athreya V, Sunagar K, Selvaraj VM, Ramakrishnan U. 2009. A panel of

microsatellites to individually identify leopards and its application to leopard monitoring

in human dominated landscapes. BMC Genetics 10:79. DOI: 10.1186/1471-2156-10-79.

Mondol S, Sridhar V, Yadav P, Gubbi S, Ramakrishnan U. 2015. Tracing the geographic

origin of traded leopard body parts in the indian subcontinent with DNA-based

assignment tests. Conservation Biology 29:556–564. DOI: 10.1111/cobi.12393.

Mora MS, Mapelli FJ, Gaggiotti OE, Kittlein MJ, Lessa EP. 2010. Dispersal and population

structure at different spatial scales in the subterranean rodent Ctenomys australis. BMC

Genetics 11:9. DOI: 10.1186/1471-2156-11-9.

Naha D, Sathyakumar S, Rawat GS. 2018. Understanding drivers of human-leopard conflicts


https://doi.org/10.1101/746081


in the Indian Himalayan region: Spatio-temporal patterns of conflicts and perception of

local communities towards conserving large carnivores. PLOS ONE 13:e0204528. DOI:

10.1371/journal.pone.0204528.

Nater A, Greminger MP, Arora N, van Schaik CP, Goossens B, Singleton I, Verschoor EJ,

Warren KS, Krützen M. 2015. Reconstructing the demographic history of orang-utans

using Approximate Bayesian Computation. Molecular Ecology 24:310–327. DOI:

10.1111/mec.13027.

Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. 2004. MICRO-CHECKER:

software for identifying and correcting genotyping errors in microsatellite data.

Molecular Ecology Notes 4:535–538. DOI: 10.1111/j.1471-8286.2004.00684.x.

Peakall R, Smouse PE. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic

software for teaching and research--an update. Bioinformatics 28:2537–2539. DOI:

10.1093/bioinformatics/bts460.

Perez I, Geffen E, Mokady O. 2006. Critically Endangered Arabian leopards Panthera pardus

nimr in Israel: estimating population parameters using molecular scatology. Oryx

40:295–301. DOI: 10.1017/S0030605306000846.

Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A. 2004. GENECLASS2: A

software for genetic assignment and first-generation migrant detection. Journal of

Heredity 95:536–539. DOI: 10.1093/jhered/esh074.

Prithard JK, Stephes M, Donnelly P. 2000. Inference of population structure using multilocus

genotype data. Genetics Society of America 155:945–959. DOI: 10.1111/j.1471-

8286.2007.01758.x.

Rangarajan, M. (2005). India's wildlife history: an introduction. Orient Blackswan.

Raza RH, Chauhan DS, Pasha MKS, Sinha S. 2012. Illuminating the Blind Spot: A study on

illegal trade in Leopard parts in India (2001-2010).


https://doi.org/10.1101/746081


Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite M, Berger J,

Elmhagen B, Letnic M, Nelson MP, Schmitz OJ, Smith DW, Wallach AD, Wirsing AJ.

2014. Status and Ecological Effects of the World’s Largest Carnivores. Science

343:1241484. DOI: 10.1126/science.1241484.

Roy PS, Joshi PK, Singh S, Agarwal S, Yadav D, Jegannathan C. 2006. Biome mapping in

India using vegetation type map derived using temporal satellite data and environmental

parameters. Ecological Modelling 197:148–158. DOI:

10.1016/j.ecolmodel.2006.02.045.

SEIDENSTICKER, J. 1990. Leopards living at the edge of the Royal Chitwan National Park,

Nepal. Conservation in developing countries : problems and prospects:415–423.

Selvan KM, Lyngdoh S, Habib B, Gopi GV. 2014. Population density and abundance of

sympatric large carnivores in the lowland tropical evergreen forest of Indian Eastern

Himalayas. Mammalian Biology 79:254–258. DOI: 10.1016/j.mambio.2014.03.002.

Sergio F, Caro T, Brown D, Clucas B, Hunter J, Ketchum J, McHugh K, Hiraldo F. 2008.

Top Predators as Conservation Tools: Ecological Rationale, Assumptions, and Efficacy.

Annual Review of Ecology, Evolution, and Systematics 39:1–19. DOI:

10.1146/annurev.ecolsys.39.110707.173545.

Sillero-Subiri C, Laurenson K. 2001. Interactions between carnivores and local communities:

Conflict of co-existence? Carnivore conservation:282–312.

Spong G, Johansson M, Bjorklund M. 2000. High genetic variation in leopards indicates

large and long-term stable effective population size. Molecular Ecology 9:1773–1782.

DOI: 10.1046/j.1365-294X.2000.01067.x.

Stein, A. B., Athreya, V., Gerngross, P., Balme, G., Henschel, P., Karanth, U., ... &

Laguardia, A. (2016). Panthera pardus, Leopard. The IUCN Red List of Threatened

Species 2016: e. T15954A50659089.


https://doi.org/10.1101/746081


Storz JF, Beaumont MA. 2002. Testing for Genetic Evidence of Population Expansion and

Contraction : An Empirical Analysis of Microsatellite DNA Variation Using a

Hierarchical Bayesian Model Published by : Society for the Study of Evolution Stable

URL : http://www.jstor.org/stable/306. Evolution 56:154–166.

Sugimoto T, Gray TNE, Higashi S, Prum S. 2014. Examining genetic diversity and

identifying polymorphic microsatellite markers for noninvasive genetic sampling of the

Indochinese leopard (Panthera pardus delacouri). Mammalian Biology 79:406–408.

DOI: 10.1016/j.mambio.2014.06.002.

Sunquist ME. 1983. Dispersal of Three Radiotagged Leopards. Journal of Mammalogy

64:337–341.

Terborgh J, Lopez L, V PN, Rao M, Orihuela G, Riveros M, Ascanio R, Adler GH, Thomas

D, Balbas L, Terborgh J, Lopez L, V PN, Rao M, Shahabuddin G, Orihuela G, Riveros

M, Ascanio R, Adler GH. 2001. Ecological Meltdown in Predator-Free Forest

Fragments Published by : American Association for the Advancement of Science Linked

references are available on JSTOR for this article : Ecological Meltdown in Predator-

Free Forest Fragments. Science (New York, N.Y.) 294:1923–1926. DOI:

10.1126/science.1064397.

Thapa K, Shrestha R, Karki J, Thapa GJ, Subedi N, Pradhan NMB, Dhakal M, Khanal P,

Kelly MJ. 2014. Leopard Panthera pardus fusca Density in the Seasonally Dry,

Subtropical Forest in the Bhabhar of Terai Arc, Nepal. Advances in Ecology 2014:1–12.

DOI: 10.1155/2014/286949.

Tucker JM, Schwartz MK, Truex RL, Pilgrim KL, Allendorf FW. 2012. Historical and

Contemporary DNA Indicate Fisher Decline and Isolation Occurred Prior to the

European Settlement of California. PLoS ONE 7:e52803. DOI:

10.1371/journal.pone.0052803.


https://doi.org/10.1101/746081


Uphyrkina O, Johnson WE, Quigley H, Miquelle D, Marker L, Bush M, O’Brien SJ. 2001.

Phylogenetics, genome diversity and origin of modern leopard, Panthera

pardus\ndoi:10.1046/j.0962-1083.2001.01350.x. Molecular Ecology 10:2617–2633.

Valdiosera CE, García-Garitagoitia JL, Garcia N, Doadrio I, Thomas MG, Hänni C, Arsuaga

J-L, Barnes I, Hofreiter M, Orlando L, Götherström A. 2008. Surprising migration and

population size dynamics in ancient Iberian brown bears ( Ursus arctos ). Proceedings of

the National Academy of Sciences 105:5123–5128. DOI: 10.1073/pnas.0712223105.

Valiere N. 2002. Gimlet: a computer program for analysing genetic individual identification

data. Molecular Ecology Notes 2:377–379. DOI: 10.1046/j.1471-8286.2002.00228.x.

Wang SW, Macdonald DW. 2009. The use of camera traps for estimating tiger and leopard

populations in the high altitude mountains of Bhutan. Biological Conservation 142:606–

613. DOI: 10.1016/j.biocon.2008.11.023.

Wolf C, Ripple WJ. 2017. Range contractions of the world’s large carnivores. Royal Society

Open Science 4:170052. DOI: 10.1098/rsos.170052.

Yang J, Jiang Z. 2011. Genetic diversity, population genetic structure and demographic

history of Przewalski’s gazelle (Procapra przewalskii): Implications for conservation.

Conservation Genetics 12:1457–1468. DOI: 10.1007/s10592-011-0244-7.

Zhu L, Zhan X, Wu H, Zhang S, Meng T, Bruford MW, Wei F. 2010. Conservation

Implications of Drastic Reductions in the Smallest and Most Isolated Populations of

Giant Pandas. Conservation Biology 24:1299–1306. DOI: 10.1111/j.1523-

1739.2010.01499.x.


https://doi.org/10.1101/746081


Figure legends

Figure 1: Forest cover map with leopard genetic sampling and population structure across

the Indian subcontinent. The map shows the inferred biogeographic leopard habitats based on

genetic cluster results found in this study. The STRUCTURE plot shows the partitioning of

microsatellite genetic variation (K=4) based on 13 loci data. The cluster names correspond to

the habitats across India subcontinent.

Figure 2: Demographic history of different genetic subpopulations of Indian leopards

(Panthera pardus fusca). The left panel shows the posterior distributions for population size

changes based on coalescent simulations for different leopard subpopulations using 13

microsatellite loci and the Storz and Beaumont approach. The dashed and solid lines

represent posterior distributions for ancestral and present effective population sizes,

respectively. The right panel presents the posterior distributions for the time since the

population decline started for different leopard subpopulations. The distributions have

median values (shown as vertical lines) ranging from ∼120-199 years. In both cases priors are

shown by fine dotted lines.

Figure 3: Patterns of leopard occurrence in India based on the analysis of questionnaire

surveys. The map shows a gradient of estimated cell-wise occupancy probabilities created

through spatial kriging.


https://doi.org/10.1101/746081


Table 1: Genetic diversity and genotyping error details for the leopard samples collected across Terai Arc landscape (n=56) in this study. A total of 13 microsatellite loci were used. Data from these samples have been added to earlier leopard forensic data described in Mondol et al. (2014).

Locus Repeat length NA Allelic size

range HE HO Null Allele Allelic dropout False allele HWE Reference

FCA230 2 16 44 0.87 0.69 0.18 0.001 0.005 Yes Menotti-Raymond et al. (1999)

FCA309 2 17 42 0.85 0.70 0.22 0.004 0.004 Yes Menotti-Raymond et al. (1999) FCA232 2 15 36 0.83 0.68 0.19 0.007 0.013 Yes Menotti-Raymond et al. (1999) FCA090 2 16 34 0.87 0.66 0.30 0.007 0.002 Yes Menotti-Raymond et al. (1999) FCA052 2 14 32 0.85 0.77 0.19 0.004 0.006 Yes Menotti-Raymond et al. (1999) FCA672 2 20 40 0.87 0.74 0.05 0.0 0.001 Yes Menotti-Raymond et al. (1999) FCA279 2 16 30 0.88 0.76 0.08 0.001 0.003 Yes Menotti-Raymond et al. (1999) FCA126 2 16 32 0.89 0.70 0.36 0.004 0.001 Yes Menotti-Raymond et al. (1999)

msFCA391 4 10 36 0.86 0.64 0.19 0.009 0.007 Yes Mondol et al. (2011) msHDZ170 2 13 42 0.83 0.53 0.30 0.002 0.002 Yes Mondol et al. (2011) msFCA441 4 12 52 0.82 0.52 0.23 0.006 0.003 Yes Mondol et al. (2011) msFCA506 2 19 56 0.89 0.69 0.25 0.008 0 Yes Mondol et al. (2011) msFCA453 4 7 32 0.68 0.61 0.25 0.006 0.007 Yes Mondol et al. (2011)

Mean (SD)

14.69 (3.41)

39.08 (7.71)

0.84 (0.05)

0.67 (0.07) 0.21 0.005 0.004

NA - No. of alleles, HE – Expected heterozygosity, HO – Observed heterozygosity, HWE – Hardy-Weinberg Equilibrium


https://doi.org/10.1101/746081


Table 2: Genetic differentiation (pairwise Fst and Gst) for four leopard subpopulations in the Indian subcontinent. The upper diagnonal presents the pairwise Gst values whereas the lower diagnonal presents the pairwise Fst values.

* p value =0.001

Western Ghats

Deccan Plateau-Semi Arid Shivalik Terai

Western Ghats

-- 0.023* 0.039* 0.091*

Deccan-Semi Arid

0.028* -- 0.045* 0.104*

Shivalik

0.048* 0.05* -- 0.073*

Terai

0.103* 0.115* 0.089* --


https://doi.org/10.1101/746081


Table 3: Subpopulation-wise summary statistics (based on 13 microsatellite loci) for Indian leopards.

Western Ghats (n=65) Deccan Plateau-Semi Arid (n=66) Shivalik (n=38) Terai (n=26)

Locus NA ASR HE HO NA ASR HE HO NA ASR HE HO NA ASR HE HO FCA230 13 36 0.88 0.86 10 22 0.78 0.80 08 24 0.83 0.23 05 26 0.52 0.65

FCA309 17 42 0.90 0.87 11 30 0.78 0.81 08 18 0.81 0.46 06 10 0.82 0.32

FCA232 13 36 0.85 0.84 09 18 0.68 0.72 09 26 0.78 0.42 07 26 0.78 0.46

FCA090 14 30 0.85 0.84 08 18 0.78 0.87 09 30 0.86 0.36 02 10 0.47 0.00

FCA052 12 32 0.84 0.89 11 22 0.82 0.84 08 20 0.83 0.48 06 22 0.84 0.43

FCA672 19 40 0.90 0.89 10 26 0.82 0.75 06 16 0.65 0.50 07 20 0.64 0.77

FCA279 11 26 0.81 0.75 14 26 0.87 0.81 15 28 0.90 0.67 06 18 0.78 0.92

FCA126 14 26 0.87 0.83 13 30 0.88 0.89 09 22 0.76 0.18 04 12 0.11 0.74

msFCA391 07 28 0.83 0.67 08 32 0.81 0.81 07 24 0.78 0.57 07 32 0.75 0.13

msHDZ170 09 20 0.84 0.79 10 22 0.75 0.88 10 36 0.76 0.16 02 02 0.29 0.0

msFCA441 07 36 0.75 0.79 08 28 0.65 0.55 08 40 0.82 0.38 05 28 0.66 0.40

msFCA506 12 32 0.86 0.90 17 56 0.83 0.82 09 24 0.83 0.33 06 22 0.79 0.40

msFCA453 05 20 0.63 0.67 07 32 0.69 0.80 04 20 0.65 0.36 02 16 0.43 0.14

Mean (SD) 11.77 (3.85)

31.08 (6.69)

0.83 (0.07)

0.81 (0.08)

10.46 (2.71)

27.85 (9.36)

0.78 (0.07)

0.80 (0.08)

08.46 (2.41)

25.23 (6.64)

0.79 (0.07)

0.40 (0.14)

05.00 (1.84)

18.77 (8.21)

0.65 (0.17)

0.36 (0.28)

NA - No. of alleles, ASR- Allelic size range, HE – Expected heterozygosity, HO – Observed heterozygosity,


https://doi.org/10.1101/746081


Table 4: Comparison of different demographic decline analyses results for different subpopulations of leopards across India.

Method Analysis type Demographic signal

Model Western Ghats Deccan Plateau-Semi Arid Shivalik Terai

Bottleneck

Qualitative

IAM Heterogygosity excess for 13 loci

Heterogygosity excess for 10 loci



SMM Heterogygosity excess for 01 loci




TPM Heterogygosity excess for 07 loci




M ratio

0.37 (SD 0.09)

0.38 (SD 0.09)

0.33 (SD 0.09)

0.29 (SD 0.15)

Storz-Beaumont

method

Quantitative Decline- 75% Time- ∼200 years ago

Decline- 90% Time- ∼125 years ago




https://doi.org/10.1101/746081



https://doi.org/10.1101/746081



https://doi.org/10.1101/746081



https://doi.org/10.1101/746081


Documents

Panthera pardus fusca) across Indian subcontinent · Among all the subspecies, the Indian leopard (P. p. fusca) retains the largest population size and range outside Africa (Jacobson